"""
Config file corresponding to the FCOS model trained on SIRStv1 dataset.

Train Script:

CUDA_VISIBLE_DEVICES=1 python tools/train_det.py configs/detection/fcos/fcos_r18-caffe_fpn_gn-head_1x_sirstv2_mNoCoAP.py --work-dir work_dirs/fcos_r18-caffe_fpn_gn-head_1x_sirstv2_mNoCoAP/test1

Test Script:

"""

_base_ = [
    # "../_base_/datasets/sirst_det_voc.py",
    "../_base_/datasets/sirst_det_voc_skycp.py",
    "../_base_/schedules/schedule_1x.py",
    "../_base_/default_runtime_mNoCoAP.py",
]

# model settings
model = dict(
    type="FCOS",
    data_preprocessor=dict(
        type="DetDataPreprocessor",
        mean=[102.9801, 115.9465, 122.7717],
        std=[1.0, 1.0, 1.0],
        bgr_to_rgb=False,
        pad_size_divisor=32,
    ),
    backbone=dict(
        type="ResNet",
        depth=18,
        num_stages=4,
        out_indices=(0, 1, 2, 3),
        frozen_stages=-1,
        norm_cfg=dict(type="BN", requires_grad=False),
        norm_eval=True,
        style="caffe",
        init_cfg=dict(type="Pretrained", checkpoint="torchvision://resnet18"),
    ),
    neck=dict(
        type="FPN",
        in_channels=[64, 128, 256, 512],
        out_channels=256,
        start_level=1,
        add_extra_convs="on_output",  # use P5
        num_outs=5,
        relu_before_extra_convs=True,
    ),
    bbox_head=dict(
        type="FCOSHead",
        num_classes=1,
        in_channels=256,
        stacked_convs=4,
        feat_channels=256,
        strides=[8, 16, 32, 64, 128],
        loss_cls=dict(
            type="FocalLoss", use_sigmoid=True, gamma=2.0, alpha=0.25, loss_weight=1.0
        ),
        loss_bbox=dict(type="IoULoss", loss_weight=1.0),
        loss_centerness=dict(
            type="CrossEntropyLoss", use_sigmoid=True, loss_weight=1.0
        ),
    ),
    # testing settings
    test_cfg=dict(
        nms_pre=1000,
        min_bbox_size=0,
        score_thr=0.05,
        nms=dict(type="nms", iou_threshold=0.5),
        max_per_img=100,
    ),
)

# learning rate
param_scheduler = [
    dict(type="ConstantLR", factor=1.0 / 3, by_epoch=False, begin=0, end=500),
    dict(
        type="MultiStepLR",
        begin=0,
        end=20,
        by_epoch=True,
        milestones=[8, 16],
        gamma=0.1,
    ),
]

val_evaluator = dict(
    iou_metrics=[
        "mNoCoAP",
    ],
    type="deepir.mNoCoAP_det_Metric",
)

# optimizer
# optim_wrapper = dict(
#     optimizer=dict(lr=0.01),
#     paramwise_cfg=dict(bias_lr_mult=2., bias_decay_mult=0.),
#     clip_grad=dict(max_norm=35, norm_type=2))
# optimizer
base_lr = 1.0
optim_wrapper = dict(
    _delete_=True,
    type="OptimWrapper",
    optimizer=dict(type="DAdaptAdam", lr=base_lr, weight_decay=0.05, decouple=True),
    paramwise_cfg=dict(norm_decay_mult=0, bias_decay_mult=0, bypass_duplicate=True),
)
